us_renew <- read_csv(here("data", "renewables_cons_prod.csv")) %>%
clean_names()
# make the description all lower case
# only keeo obs where "description" var contains "consumption"
# remove any obs where "description" contains "total"
renew_clean <- us_renew %>%
mutate(description = str_to_lower(description)) %>%
filter(str_detect(description, pattern = "consumption")) %>%
filter(!str_detect(description, pattern = "total")) # get rid of total
renew_date <- renew_clean %>%
mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>%
mutate(month_sep = yearmonth(yr_mo_day)) %>% # pull just the year and month from that column
mutate(value = as.numeric(value)) %>%
drop_na(month_sep, value)
# make a version where I have the month and year in separate column
renew_parsed <- renew_date %>%
mutate(month = month(yr_mo_day, label = T)) %>%
mutate(year = year(yr_mo_day))
# renew_gg <- ggplot(data = renew_date, aes(x = month_sep, y = value)) +
# geom_line()
#
# renew_gg
#这个图非常不说明问题,看下面
renew_gg <- ggplot(data = renew_date, aes(x = month_sep, y = value, group = description)) +
geom_line(aes(color = description))
renew_gg
Updatingcolors with paletteer palettes
renew_gg +
scale_color_paletteer_d("calecopal::conifer") # view(palettes_d_names)这行码给你看所有颜色的组合
renew_ts <- as_tsibble(renew_parsed, key = description, index = month_sep)
Let’s look at our ts data in a couple different ways
renew_ts %>% autoplot(value)
renew_ts %>% gg_subseries(value)
renew_ts %>% autoplot(value)
renew_ts %>% gg_subseries(value)
# renew_ts %>% gg_season(value) 这个用不了就用gg自己做
ggplot(data = renew_parsed, aes(x = month, y = value, group = year)) +
geom_line(aes(color = year)) +
facet_wrap(~ description,
ncol = 1,
scales = "free",
strip.position = "right")
hydro_ts <- renew_ts %>%
filter(description == "hydroelectric power consumption")
# Explore:
hydro_ts %>% autoplot(value)
hydro_ts %>% gg_subseries(value)
# hydro_ts %>% gg_season(value)
ggplot(hydro_ts, aes(x = month, y = value, group = year)) +
geom_line(aes(color = year))
hydro_quarterly <- hydro_ts %>%
index_by(year_qu = ~ yearquarter(.)) %>% # monthly aggregates
summarise(
avg_consumption = mean(value)
)
head(hydro_quarterly)
## # A tsibble: 6 x 2 [1Q]
## year_qu avg_consumption
## <qtr> <dbl>
## 1 1973 Q1 261.
## 2 1973 Q2 255.
## 3 1973 Q3 212.
## 4 1973 Q4 225.
## 5 1974 Q1 292.
## 6 1974 Q2 290.
dcmp <- hydro_ts %>%
model(STL(value~season(window = 5)))
components(dcmp) %>% autoplot()
hist(components(dcmp)$remainder)
now look at ACF fuction
hydro_ts %>%
ACF(value) %>%
autoplot()
hydro_model <- hydro_ts %>%
model(
ARIMA(value)
) %>%
fabletools::forecast(h = "4 years")
hydro_model %>% autoplot(filter(hydro_ts, year(month_sep) > 2010))
world <- read_sf(dsn = here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
layer = "TM_WORLD_BORDERS_SIMPL-0.3")
mapview(world)